1. Technical Field
The present disclosure relates to time series images and, more specifically, to an interaction method for regions-of-interest in time series images.
2. Discussion of Related Art
Medical images may be either two-dimensional or three-dimensional and may be comprised of a series of image frames that together show a progression over time. After acquisition, medical images may be segmented such that the bounds of various anatomical structures are correctly identified. Segmentation may be fully manual, fully automatic, or partially automatic.
In manual segmentation, a human user is responsible for delineating the bounds of the anatomical structure. In automatic segmentation, a computer is used to delineate the bounds of the anatomical structure without human intervention. However, most commonly, the user provides some basic identification of the anatomical structure and computer vision techniques are used to work out many of the details involved with discovering the full bounds of the anatomical structure.
Many approaches to semi-automatic segmentation provide a user an opportunity to modify a computer-derived segmentation so that errors may be corrected or ask the user for input that initializes the automatic segmentation procedure. However, where the medical images being segmented are comprised of a series of image frames acquired over a span of time, it may be time consuming and tedious for the user to manually modify each image frame. Some approaches therefore use user modifications as a basis for re-executing the segmentation algorithm. However, such approaches may be computationally expensive and, where multiple image frames have been manually modified, re-executing the segmentation algorithm based on one manually modified frame may overwrite manual modifications made to other image frames.
These concerns may be exacerbated for medical images that are both three-dimensional and include a series over time. Moreover, it is possible that the medical images have a higher dimensionality than three-dimensions, for example, where functional data, flow data, or other characteristics are superimposed over the two or three spatial dimensions of the medical image.